hub/main.py

from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
import subprocess
from pathlib import Path

from hub.imports.weather_factory import WeatherFactory
from pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
from pv_assessment.pv_system_assessment import PvSystemAssessment
from pv_assessment.solar_calculator import SolarCalculator

input_file = "data/cmm_test_corrected.geojson"
demand_file = "data/energy_demand_data.csv"
# Define specific paths for outputs from SRA (Simplified Radiosity Algorith) and PV calculation processes
output_path = (Path(__file__).parent.parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
sra_output_path = output_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
pv_assessment_path = output_path / 'pv_outputs'
pv_assessment_path.mkdir(parents=True, exist_ok=True)
city = GeometryFactory(
               "geojson",
               input_file,
               height_field="height",
               year_of_construction_field="contr_year",
               function_field="function_c",
               adjacency_field="adjacency",
               function_to_hub=Dictionaries().montreal_function_to_hub_function).city
ConstructionFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
ResultFactory('archetypes', city, demand_file).enrich()
# Export the city data in SRA-compatible format to facilitate solar radiation assessment
ExportsFactory('sra', city, sra_output_path).export()
# Run SRA simulation using an external command, passing the generated SRA XML file path as input
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
# Enrich city data with SRA simulation results for subsequent analysis
ResultFactory('sra', city, sra_output_path).enrich()
# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
tilt_angle = 37
solar_parameters = SolarCalculator(city=city,
                                   surface_azimuth_angle=180,
                                   tilt_angle=tilt_angle,
                                   standard_meridian=-75)
solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
# Assignation of Energy System Archetypes to Buildings
#TODO this needs to be modified. We should either use the existing percentages or assign systems based on building
# functions
for building in city.buildings:
  building.energy_systems_archetype_name = 'Grid Tied PV System'
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
  electricity_demand = HourlyElectricityDemand(building).calculate()
  PvSystemAssessment(building=building,
                     pv_system=None,
                     battery=None,
                     electricity_demand=electricity_demand,
                     tilt_angle=tilt_angle,
                     solar_angles=solar_angles,
                     pv_installation_type='rooftop',
                     simulation_model_type='explicit',
                     module_model_name=None,
                     inverter_efficiency=0.95,
                     system_catalogue_handler=None,
                     roof_percentage_coverage=0.75,
                     facade_coverage_percentage=0,
                     csv_output=False,
                     output_path=pv_assessment_path).enrich()

print("done")